Trends in Peterson 2003

Peterson 2003 stated:

Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures.

Last week, Peterson sent me a list of the 289 sites used in this study, together with the classification into urban and rural. As I noted previously, there are many puzzles in the allocation of sites to urban and rural with many “urban” sites seemingly being at best very small towns and, in some cases, rural themselves. So, in that sense, it would seem unsurprising if Peterson didn’t observe any difference between the two networks.

Assuming nothing, I downloaded raw daily data for 282 out of 289 sites. (The other 7 sites either had id number discrepancies or were not online at GHCND.) From this, I calculated average monthly TMAX and TMIN temperatures for all the sites and then calculated 1961-1990 anomalies. I then calculated simple averages of the “raw” anomalies for the two networks BEFORE any jiggery-pokery. Even if all the subsequent adjustments are terrific, from a statistical point of view, it’s always a good idea to see what your data looks like at the start. Here is a plot (with a 24 month smooth.)

As you see in the bottom panel, there is an observable trend in the difference between Peterson-urban and Peterson-rural sites. The delta over 100 years is just under 0.7 deg C.

You would think that this would have been one of the first tests that Peterson would have carried out and his failure to either carry out this test or report such results if the procedure were carried out is noticeable.

Peterson’s articles describes a series of adjustments: for elevation, latitude, time-of-observation, MMTS. Not all of these adjustments are relevant to an anomaly-based comparison. For example, the adjustment for elevation and latitude is relevant to a direct comparison of urban and rural absolute temperatures, but not for a comparison of anomaly trends. Peterson cites literature (Quayle et al 1991) which states that MMTS introduction has minimal effect on averages (although it increases the TMIN and reduces the TMAX). So this would not account for the difference.

Peterson reported on TOB as follows:

The percentage of stations reading in the afternoon is about the same for rural (33%) as urban (35%). However, rural stations have a higher percentage of a.m. readers (53% versus 37%) and a lower percentage of midnight readers (14% versus 27%) than urban stations.

Again for trends, the salient point is the change in proportions, rather than the specific mechanism. The implication of Peterson’s analysis would seem to be that the 0.7 deg C delta in Peterson urban-Peterson rural differential is not due to the effect of urbanization on the urban sites but related somehow to the higher present proportion of morning to midnight readers in the rural network.

Readers should note that Peterson does not carry out TOB adjustments based on documented changes in observation time (which USHCN users might assume). Instead Peterson has used a procedure attributed to DeGaetano BAMS 2000, which purports to estimate observation time based on the properties of the data itself. The DeGaetano procedure, as with so many of these recipes, is not a statistical procedure known to statistical civilization off the island. You can’t go to a statistics textbook and learn its properties. There is no systematic presentation of DeGaetano-adjusted TOBS series against USHCN adjusted series.

However, regardless of the merits of the DeGaetano adjustment, I think that it’s incorrect for Peterson to say that there is no observable difference in urban and rural trends in his network. There is a substantial difference in trends in the “raw” data, which should have been reported. He believes that this difference is due to TOBS changes based on De Gaetano adjustments, but it’s possible that there is some other explanation for the difference, including the obvious candidate – UHI.

UPDATE

This comparison actually gets a little worse.

In the figure below, I’ve calculated the average unadjusted temperature for actual cities, rather than places like Snoqualmie Falls. My criterion for inclusion in this calculation is whether the city has a major league sports franchise and includes a variety of mostly small market cities: Milwaukee, Sacramento, Orlando, San Antonio, Cincinnati, San Diego, Seattle, Salt Lake City, New Orleans, plus a couple of larger places: Detroit, Philadelphia, Dallas. To my knowledge, no sports franchises are considering re-location or expansion to Snoqualmie Falls, Hankinson, Pine Bluff or the various other supposedly “urban” sites that dilute the Peterson network.

In this data set that supposedly shows the following:

Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures.

actual cities have a very substantial trend of over 2 deg C per century relative to the rural network – and this assumes that there are no problems with rural network – something that is obviously not true since there are undoubtedly microsite and other problems. At the very end of the graphic, the change levels off – I wonder if that might indicate increased settlement effects at rural sites.

Figure 2. Comparison of Peterson Sites with Major League Sports Franchises to Rural Network

Now this doesn’t prove anything one way or the other about other networks – other than there is a need to be wary. However, the notion that Peterson 2003 is a sustainable authority for the IPCC proposition that “rural station trends were almost indistinguishable from series including urban sites” seems increasingly difficult to accept.

The linearity in the difference (plot 3) is remarkable. Steve, have you thought to correlate that difference with the growth of the US urban population over the same period?

#1 — In the context of the careful studies posted at CA, we’ve also now learned that Peterson, like so very many others in climate science, has tailored his analysis and conclusions to be consistent with the prevailing explanatory model, namely AGW. In my own years of experience in science, never have I seen such widespread or such egregious tendentious thinking. It’s ruined the field.

#3 Peterson’s data shows not only that UHIs exist, but also that as, Hans pointed out, UHIs are generally growing in magnitude. This data was the basis for a paper which concluded that the UHI effect was negligible.

#1 I would agree with you that the satellite night light based selection criteria is not perfect, but no selection criteria is perfect. Some are certainly better than others, but the definition of what classifies a site as “rural” is not trivial. I assume that most people expect a “rural” site to be one which is relatively uneffected by urbanization. Although this is easy to contemplate, it is difficult implement.

I’ve just done a subanalysis in which I calculate the average for sites which have a major-league sports franchise (and the ones in the Peterson “urban” network are small-market Milwaukee, Sacramento, San Antonio). It’s amusing. I’ll post it up in a few hours.

Both Peterson’s “urban” and “rural” data sets show warming in the 1930s and a cooling trend from 1940 to 1980. These data need much more “adjustment” to bring them in line with the global temperature according to Phil Jones.

Every time you look at one of these studies in detail, the same result comes out. That of, data manipulation in order to show a particular result favourable to the AGW proposition.

Which is, itself, something that is used in the medical field, for example, as the final scientific proof regarding a particular treatment. The meta-study or meta-analysis (or combining the results of as many studies as possible) is used as substantial evidence that treatment X or drug Y is medically helpful or not.

Maybe it is time, Steve, for the meta-analysis paper on climate change. Maybe Roger Piekle would be a good co-author.

This analysis and data should be published as a matter of urgency. Has a worldwide rural v urban analysis been done? The 2007 June July global temp data will I expect, be very interesting judging from weekly SH, tropics and some NH observations

I’ve updated this post to show a difference of 2 deg per century in trend in the raw data converted to anomalies between stations in the Peterson network with major league sports franchises and Peterson’s rural network.

Snoqualmie Falls has historically been the epitome of ‘Rural’. I’d just note that in the last 10 years it has essentially been changing into a suburb of Seattle. This isn’t to say it is ‘Urban’, just that an awful lot of pavement has been slapped down in that general vicinity. So it will depend on the siting. Interstate-90 was widened going out of Seattle and through Snoqualmie Pass, to the point that (at non-rush hours) you’re on a 70mph, half-hour trip to Seattle.

CNN reports that after reading this blog and seeing the correlation between major league baseball teams and temperature trends, governor Arnold Schwarzenegger is going to shut down all five major league stadiums in California as part of his fight against global warming.

As a result, the Los Angeles Angels are looking to move to Snoqualmie Falls where they will be known as the Fallen Angels.

Seriously, are scientists like Peterson so used to no one checking their claims that they will put out just any ideas that sound good? Or are their positions so secure that they really don’t care if they publish stuff that can be easily rebutted? With their own data no less! Before I get gobsmacked, I am going to launch a preemptive strike by finding a couple of gobs and smacking them first.

Looking at your list of major league cities it seems that most of these cities have only recently grown to the size that they can support major sports franchises. Perhaps the criteria should be growth rate rather than urban-ness. Last month when you looked at Central Park in New York there was very little change in the raw temperature data for almost 50 years.

For example, the adjustment for elevation and latitude is relevant to a direct comparison of urban and rural absolute temperatures, but not for a comparison of anomaly trends.

because I’ve been racking my brain for a while trying to figure out why the trends needed to be adjusted for elevation. Made no sense to me. Does anyone out there have a clue why you’d adjust for elevation?

its clear from your updated analysis that peterson is correct. There is no UHI effect but there is a clear Sports Franchise Island (SFI) effect.

Now all you’ve got left to prove is that Yule was wrong and that there is a clear real correlation between alcoholism and Church of England marriages and similarly that Hendry was also wrong and that there is also an obviously real correlation between inflation and rainfall (ask Gordon Brown and the bank of England).

Why any reasonable scientist should ever doubt these correlations I do not know as these obvious truths have been known to climate scientists and the IPCC for decades.

19, Al,
The line between “urban” and “rural” is not easy to draw. Even small towns have a detectable UHI, thus for the purposes of Peterson’s paper, I suspect that Snoqualmie Falls should be listed as urban, not rural.

From what I have gathered, after Oke, any claims of global warming would have to find a way to filter out UHI; or such claims would be immediately discounted. If you read what Peterson and others are saying:

For example, Hansen et al. (JGR, 2001) adjusted trends in urban stations around the world to match rural stations in their regions, in an effort to homogenise the temperature record.

This quote from Wiki would be your answer. They want a homogenized data set. Their claim is that this takes care of the UHI, and other site problems. They have noted that the network is not all that it should be. But claim as Peterson, summarized by Wiki:

“Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures.” This was done by using satellite-based night-light detection of urban areas, and more thorough homogenisation of the time series (with corrections, for example, for the tendency of surrounding rural stations to be slightly higher, and thus cooler, than urban areas). As the paper says, if its conclusion is accepted, then it is necessary to “unravel the mystery of how a global temperature time series created partly from urban in situ stations could show no contamination from urban warming.” The main conclusion is that micro- and local-scale impacts dominate the meso-scale impact of the urban heat island: many sections of towns may be warmer than rural sites, but meteorological observations are likely to be made in park “cool islands.”

The summary in Wiki, if true, makes Steve’s graph even more gobsmacking. It also indicates why homogenization would be needed to get rid of UHI, rather than finding that UHI was not measureable. A collolary is that

meteorological observations are likely to be made in park “cool islands.”

appears to be utter nonsense. As in, the cooling effect of parks, and watering of said parks, do little if anything to offsite UHI.

It makes the IPCC claims as summarized in Wiki on UHI and global warming more than a little suspect. I would be interested in hearing an explanation.

Re: Fig 2 and the disappearing variance between “urban” and “rural” in recent decades. My guess is that two effects are being mainfested. First, the urban growth so clearly evident in the early decades had reached the point by roughly the 1980s that further growth had significantly reduced UHI growth effect. Second there was a surge of relative urbanization of hitherto rural sites, particulary in the 1990s. There is a clear upturn in thr rural sites from roughly 1990. Anyone who has travelled a lot in Appalachia would be aware of the large effect of the increase in prosperity of the nation during the Clinton years. It is much more evident in relatively poor rural areas than in urban areas. Rural towns tended to grow significantly during these years. All of the trends displayed in Fig 2 tend to support the UHI hypothesis. Murray

Remember the 50’s was the start of the suburbanization of America and you can see the difference decreasing at that time. The sixites was the time of the suburban mall and the collapse of the downtown as a shopping area. I live just outside of the Washington, D.C. beltway and it ain’t rural no more. Rural areas have more heat sources and more asphalt and concrete than they used to. Areas where there were farms just a few decades ago are now suburbs, so you would need to subdivide the rural into suburb and truly rural. Cities are becoming more popular for living so I would expect the rate to increase in the cities again.

re 2 Steve, My comment was not a criticism of your analysis. You were able to tease out a UHI signal out of Peterson’s data, yet Peterson was not. So then what is the major flaw in Peterson’s work? Why can’t Peterson find a UHI fingerprint?

It seems to me that Peterson does not have a legitimate method of distinguishing “urban” sites from “rural” sites. I have not done the kind of careful analysis we rely on your for. But I see a classification system that allows a site close to a nuclear power station

Wedgefield S.C. is classed as urban to lack credibility. I just haven’t seen a good definition of rural or urban.

I have spent much time water skiing and sailing on the cooling impoundment (TPTB don’t like to call Lake Robinson a Lake) at the H. B. Robinson Nuclear Generating Station near Hartsville S.C. so I am familiar with the enormous amounts of waste heat discharged by a nuclear generating plant.

About ten years ago, I remember reading about results like this where the data show no warming trend if you select a subset of the data that is presumably high-quality. I remember reading that the warming trend was much smaller if you looked at (1) just U.S. data (do you really want to include Somali data), (2) just rural data (which is what you are doing here), and (3) just temperature readings done by the military. This last one sounds like the most interesting. Presumably, the military has very high quality standards and does the best job of following the appropriate protocols consistently over time and would take its measurements in locations least susceptible to UHI. I suggest you try it.

Somehow, though, those analyses dropped out of sight. I thought it was because they had been discredite. (Even World Climate Report didn’t mention them anymore.) Any ideas why they dropped out of sight? Does the satellite record undermine these results? Would we expecte satellited data to be significantly affected by UHI?

The graphs Steve posted here are such smoking guns that I can hardly see through the smoke. To get this
published, however, the reviewers would require “proper” adjustments, not the raw data (I’m guessing)
It would be very important to get this out. Hope it can be.

Is there not a special correction that was used to adjust the temperature records for cities with major league franchises? As I imperfectly recall it was from TheGateANO factor or effect of the unnatural gathering of crowds in an urban setting and the heat they generate: The Gate’s Anthropogenic Natural Outputs. I believe I saw it in an issue of BUMS 2000. It was ground breaking work to be sure.

The thing that strikes me most about the Urban & Rural graphs is how close the high frequency portions are. This can be seen in that the difference graph has a very low amptitude compared to the Urband and Rural ones. Since these are presumably disjoint sets of data from disparate parts of the country, they must be showing general short-term changes in the weather/climate of the county. I’m not enough aware of how this should be stated in statistical terms, but could you state it (with numbers), in terms which would have statisticians nodding their head in agreement?

Would it be more useful to have a measure that combines the growth rates of both population and electrification? A sharp population growth in the 19th and early 20th centuries would be occurring in a relatively low energy milieu.?

There is some interesting data at href=”http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html”

It shows electrical use per state and year from 1990 to 2005. The U.S. total increased from about 3 billion KWH to 4 Billion
KWH in that time. While it doesn’t help separating the individual locations it might be useful on a state by state basis.

Two programs in the 20th century had a powerful effect on Urban/Rural relationships. The first was
the Rural Electrification Program of the 1930s powered by the new Tennessee Valley Authority and the second was
the Eisenhower Interstate System of the 1950s and 1960s. Copied from the Federal Highway Administration site,
“Last year marked the 50th anniversary of the Eisenhower Interstate System. Since its initiation on June 29, 1956,the Dwight D. Eisenhower System of Interstate and Defense Highways has had a tremendous impact on our nation.”

Between them those two programs spurred the urbanization of many previously rural locations.

It might be instuctive to see if temperature changes closely mirror the geographic expansion of those two programs.

There is some discussion around categorising stations (mainly urban versus rural) and asking whether category makes any difference to Peterson’s results. I wonder if this could be approached from a slightly different angle.

The CA analysis shows how some stations contribute more to the observed trend than others. Rather than starting off by placing stations into categories, the stations could be ranked in order of individual T trend. From there it might be possible to look more generally at how station characteristics are distributed across the ranked set. We are looking to make sure characeristics are evenly distributed.

OK, lots of work in this, but let me press-on anyway.

Let’s say the top contributors are selected (20 or 30 stations showing the greatest positive trend). How much of Peterson’s trend is explained by these stations? Do they make a disproportionate contribution? If so, why?

If we take a closer look at the “top stations” (assuming the information is available) and find that they have nothing much in common (no better than random selection) we could conclude that categorising stations is not a very useful exercise. Peterson’s position would be reasonable.

However, we might find relevant common factors among the top stations (recent urbanisation for example), particularly if we find the incidence of any factor is significantly different to the population. If so, there would be reason to suspect bias in the statistics and it ought to be possible to improve Peterson’s analysis by removing all stations with the same characteristic (OK, at the expense of increased error bands I suspect). It would be interesting to examine how much of the observed trend would then remain.

It might even be useful to carry out the same exercise for the lowest contributors (individual stations showing the lowest trend). Why do they contribute so little? Do they have common characteristics or do they look like the result of random selection?

These are just some thoughts. It would be good to get some more informed views.

I want to reiterate JerryB’s point in comment #7. Peterson did not look for trends. He looked for temperature differences between groups of sites designated as rural and urban for the individual years between 1989 and 1991 after correcting for obvious biases like altitude of the measurement station (higher is colder). If you look at Steve’s graphs above, Peterson’s conclusion of no difference is almost certainly technically correct. I suspect, given what looks to be a high correlation between Peterson’s rural and urban averages, that the conclusion would be the same for any consecutive three years between 1900 and 2000. The conclusion would probably hold for temperatures post 1980 using Steve’s Major League Cities vs rural grouping. It’s therefore not surprising that the paper passed peer review. It is, though, incredibly misleading about the influence of UHI on long term temperature trends if you don’t pay attention to what Peterson actually did.

Maybe this has already been brought up…It seems confusing that “urban” sites would be cooler than “rural” sites until the 70s in your first graph and until the 80s in the second graph. How is this explained? Are the “rural” sites predominantly from the south and the “urban” sites predominantly from the north? Are there other regional differences that would explain why urban sites were cooler during those time periods? Are the unadjusted trends presented here not confounded with other regional variations?

Also, it would seem that “major league sports franchise” requirement would induce a heavy coastal bias as well as an east coast bias to the urban sites. It would present a lack of urban sites in alaska, hawaii, oregon, idaho, montana, wyoming, new mexico, north dakota, south dakota, nebraska, oklahoma, iowa, arkansas, missisippi, alabama, south carolina, kentucky, virginia, west virginia, delaware, conneticut, rhode island, vermont, new hampshire, and maine. It would appear that this requirement would introduce a significant bias into your analysis. How did you take this into consideration?

It seems from common sense that the warming trend during the 20th century would not be uniform across the country from the marine west coast climate of the NW to the humid subtropical climate of the SE and the midlatitude desert of the SW to the humid continental climate of the NE and the semiarid steppe climate in between. Thus if there are location and/or climate region biases between the rural and urban stations, your quick comparison would not accurately isolate the UHI effect. Maybe you need some “jiggery-pokery” to remove such a bias?

I think this explains why there appears to be an upward adjustment in the older data at some of these sites.

During this time, many sites were relocated from city locations to airports and from roof tops to grassy areas. This often resulted in cooler readings than were observed at the previous sites. When adjustments were applied to correct for these artificial changes, average US temperature anomalies were cooler in the first half of the 20th century and effectively warmed throughout the later half.

obvious biases like altitude of the measurement station (higher is colder).

Although this may be true in places, and it certainly is if you go up high enough (1,000+ m difference), you can get warmer with elevation. The coast of California is an area where the temperature increases as you go up in elevation (and thus further from the cold waters of the Pacific.)

Anyone who has driven from the small town of San Simeon up to the top of La Cuesta Encantada (otherwise known as Hearst Castle) will have experienced this effect.

…you can get warmer with elevation. The coast of California is an area where the temperature increases as you go up in elevation (and thus further from the cold waters of the Pacific.)

Indeed. Somehow I never managed to get to Hearst Castle while I lived in CA. *sigh* But I was thinking of altitude at a specific geographic location like moving from the top of a pole or building to the ground and vice versa. On a calm night with the normal temperature inversion it should actually get warmer with a higher observation height, at least up to 100m or so. In fact, Pielke, Sr. has a paper on how this rather steep gradient can bias trends in Tmin.

The urban-franchise plot is not necessarily colder than the rural plot. The plot shows the ‘anomaly’, in this case the delta from the 1961-1990 mean. What you can read from the plots is that urban-franchise stations were much colder early in the century compared to the tempewratures 1961-1990, less so for the rural stations. The comment in #33 explains this fairly well.

The analaysis by Steve McIntyre isn’t trying to represent the entire US, nor even the 48 contiguous states. It is showing that the data used by Peterson do indeed have distinct differences between rural and urban stations based on an alternate definition of urbanness. The take-home point is that contrary to Peterson’s analysis there is an urban imprint in the data that will influence the overall calculated temperature trends.

Whether or not any jiggery-poker is needed is in determining the extent of this urban imprint and whether any of the urban data has a recoverable climate signal. In that case it would be very useful to move beyond the 289 stations and look at all the USHCN stations. The fact that a strong difference can be seen in a limited sampling suggests ripe opportunities for follow up analysis.

To rephrase it more simply, I was observing noticeable geographical differences between rural and urban stations that are not accounted for in Steve’s “major city” analysis in #47. It is clear to see that this analysis has not isolated the urban vs. rural variable. It would be premature for Steve to imply that the differences in his update urban and rural trends are in any way due to UHI. Maybe if Steve didn’t introduce additional geographic variability, his latter analysis would have some use.

#53, I thought that one of the advantages of using anomolies is that such differences (geographical) become moot. If this is not true, such factors as 1)typical and exceptional wind direction and speed; 2) lat/long, altitude and slope with in 50 mile radius, plus confounding from 1); 3) makeup of immediate, micro, and meso ground cover; 4) diffences in heat capacity and latency; and 5) a whole host of other effects would have to be accounted for just to get started.

ks, those are anomolies not actual temperatures. The values are the difference of the given data point from a value calculated from those same sites. Rural against the mean of the Rural and Urban against the mean of the Urban. The zero point on each graph is different (in absolute value) and they can’t be compared they way your trying to compare them. They show the trends not the absolute values. If you had the two averages used you could adjust one of the charts to the same scale and then make a valid comparison but not what your trying to do now.

Anomolies are not uniform across the lower 48 and the analysis does not account for that. The selection of major sports teams present as the definition for urban skews the selection heavily towards the east coast. The anomolies for the east coast vary compared to anomolies for other regions. Just look at the pictures http://data.giss.nasa.gov/gistemp/graphs/monthly_maps_lrg.gif

Thank you very much for you re-analysis of peterson’s work.
I live in the Po Valley in Italy where are concentrated the most important cities as well as industries. Anticipated blooming is considered one of the more convincing signs of AGW. It is quite impossible to convince collegues that changes in land-use and UHI could be major responsible for this effect (some are ideologically driven). 4AR clearly report Peterson’s findings as the ‘real true’ to be utilised, and my comments (as an urban microclimatologist) were ‘cancelled’. So, thank you again to have ‘reloaded’ the gun

The first thing anyone wishing to make worthwhile comments on climate related topics is to obtain the original numerical data and do appropriate analyses for him/her self. Take this advice and apply some of the techniques of industrial quality control to the data (regarding them as sequential observations of “product quality”) and you will readily show some important truths about the data that remain up to now unremarked upon by professional climatologists. The major one is that climate (temperature) at a given site changes (surprise, surprise) but also that it usually changes in a series of steps rather than gradually. In many cases these change points are astonishingly abrupt, perhaps even taking place in a period of a very few months (even one month,I suspect). I can post summaries of this approach in the form of GIFs which will demonstrate clearly some striking changes that have taken place in the past. The future is a closed book, and those who purport to predict what will have happened 50 years from now are surely guessing, probably with an eye on the beliefs of their funders.

Steve, I agree with #31; the graph looks like cities started colder than the countryside. Could you please regraph the data to correct this? You could, for example, plot both groups vs the average of the whole 289 station set.

There’s an interesting project here run by the Royal Meterological Society using schoolchildren to measure UHI at a single point in time across a number of UK cities. Unfortunately they don’t seem to have asked for windspeed measurements at the same time, although from their documentation they are aware of the claims about the effect being negligible.

The issue of “rural” vs. “urban” I guess should be explored using elementary exploratory techniques like PCA/PCR plots? A simple set of such gifs may demonstrate clearly some interesting clusterings among stations, like “urban” vs “rural”.

[…] I haven’t done any further tests on this yet, but spot checking of some clusters seems quite practical. Though the basis for selection of these comparisons seems very unclear. See related posts here and here.) […]

[…] Peterson's "Urban" Sites I posted up the list of 289 sites from Peterson 2003 purporting to show that the difference between “urban” and rural sites was negligible. (See related posts here and here.) […]

[…] Steve McIntyre challenged NOAA’s Peterson (2003), who had said, “Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures” by showing that the difference between urban and rural temperatures for Peterson’s own 2003 station set was 0.7C and between temperatures in large cities and rural areas 2C (below, enlarged here). […]